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Advances in Acoustics and Vibration
Volume 2012 (2012), Article ID 172625, 10 pages
http://dx.doi.org/10.1155/2012/172625
Research Article

Multiengine Speech Processing Using SNR Estimator in Variable Noisy Environments

1Department of Enterprise and Higher Education, International Turnkey Systems, ITS Tower, Mubarak Al Kabeer Street, P.O. Box 26729, Safat, Kuwait City 13128, Kuwait
2Department of Systems and Computer Engineering, Carleton University, 1125 Colonel By Drive, Ottawa, ON, Canada K1S 5B6

Received 14 August 2011; Accepted 26 October 2011

Academic Editor: Akira Ikuta

Copyright © 2012 Ahmad R. Abu-El-Quran et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We introduce a multiengine speech processing system that can detect the location and the type of audio signal in variable noisy environments. This system detects the location of the audio source using a microphone array; the system examines the audio first, determines if it is speech/nonspeech, then estimates the value of the signal to noise () using a Discrete-Valued Estimator. Using this value, instead of trying to adapt the speech signal to the speech processing system, we adapt the speech processing system to the surrounding environment of the captured speech signal. In this paper, we introduced the Discrete-Valued Estimator and a multiengine classifier, using Multiengine Selection or Multiengine Weighted Fusion. Also we use the as example of the speech processing. The Discrete-Valued Estimator achieves an accuracy of 98.4% in characterizing the environment's . Compared to a conventional single engine system, the improvement in accuracy was as high as 9.0% and 10.0% for the Multiengine Selection and Multiengine Weighted Fusion, respectively.